Sarcasm Detection

The goal of Sarcasm Detection is to determine whether a sentence is sarcastic or non-sarcastic. Sarcasm is a type of phenomenon with specific perlocutionary effects on the hearer, such as to break their pattern of expectation. Consequently, correct understanding of sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and, frequently some real world facts.

Source: Attentional Multi-Reading Sarcasm Detection

Papers

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Showing 81–90 of 266 papers

Title	Date	Tasks	Status
Cross-Cultural Transfer Learning for Text Classification	Nov 1, 2019	ClassificationGeneral Classification	—Unverified
A Survey of Multimodal Sarcasm Detection	Oct 24, 2024	Sarcasm DetectionSurvey	—Unverified
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling	Jul 1, 2022	Feature Engineeringregression	—Unverified
Context-Aware Sarcasm Detection Using BERT	Jul 1, 2020	Sarcasm Detection	—Unverified
connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection	Jul 1, 2022	Sarcasm DetectionSentence	—Unverified
Assessing how hyperparameters impact Large Language Models' sarcasm detection performance	Apr 8, 2025	Natural Language UnderstandingSarcasm Detection	—Unverified
Computational Sarcasm Analysis on Social Media: A Systematic Review	Sep 13, 2022	Sarcasm DetectionSentiment Analysis	—Unverified
Computational Sarcasm	Sep 1, 2017	Sarcasm DetectionSentiment Analysis	—Unverified
ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets	Apr 1, 2021	Contrastive LearningSarcasm Detection	—Unverified
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis	Oct 1, 2022	Contrastive LearningMultimodal Sentiment Analysis	—Unverified

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All datasets BIG-bench (SNARKS)FigLang 2020 Reddit Dataset FigLang 2020 Twitter Dataset SARC (all-bal)SARC (pol-bal)iSarcasm MUStARD SARC (pol-unbal)WITS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	PaLM 2(few-shot, k=3, CoT)	Accuracy	84.8	—	Unverified
2	PaLM 2 (few-shot, k=3, Direct)	Accuracy	78.7	—	Unverified
3	PaLM 540B (few-shot, k=3)	Accuracy	78.1	—	Unverified
4	BLOOM 176B (few-shot, k=3)	Accuracy	72.47	—	Unverified
5	Bloomberg GPT (few-shot, k=3)	Accuracy	69.66	—	Unverified
6	GPT-NeoX (few-shot, k=3)	Accuracy	62.36	—	Unverified
7	Chinchilla-70B (few-shot, k=5)	Accuracy	58.6	—	Unverified
8	Gopher-280B (few-shot, k=5)	Accuracy	48.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT+Aspect-based approaches	F1	0.74	—	Unverified
2	RoBERTa_large - (Separated Context-Response)	F1	0.72	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa_large (Context-Response)	F1	0.77	—	Unverified
2	BERT	F1	0.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CASCADE	Accuracy	77	—	Unverified
2	Bag-of-Bigrams	Accuracy	75.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Bag-of-Bigrams	Accuracy	76.5	—	Unverified
2	CASCADE	Accuracy	74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa + Mutation Data Augmentation	F1-Score	0.41	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MUStARD++	Precision	70.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Bag-of-Words	Avg F1	27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BART	R1	36.88	—	Unverified